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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 491–499Cite as

Multi-view Multi-exposure Image Fusion Based on Random Walks Model

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

Abstract

By the constraints of the scenarios and cameras, we can hardly get a fully detailed image due to the preperties of exposure. Although some algorithms were proposed to deal with such problems these years, they still have some strict restrictions on the input images which must be captured from the same sight simultaneously. In this paper, we present a method which fuses multi-exposure images from different views. Some techniques in the field of stereo are introduced to deal with feature points matching, and a random walks framework is used to calculate the probabilities of one walking randomly from an unknown point to seed points. These probabilities reveal luminance changes of unknown pixels, and then we can enhance the intensities to make a fusion. Our experiments demomstrate that this method generates accurate results in most situations.

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Acknowledgement

The work is supported by National Program on Key Basic Research Project (973 Program).

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Authors and Affiliations

  1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xiao Xue & Yue Zhou

Authors
  1. Xiao Xue
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  2. Yue Zhou
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Corresponding author

Correspondence to Yue Zhou .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Xue, X., Zhou, Y. (2017). Multi-view Multi-exposure Image Fusion Based on Random Walks Model. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_36

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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